With excellent models at hand, the focus has now shifted to the quality and quantity of the underlying data. New datasets such as the Open Catalyst 2020 and 2022 datasets for adsorbates on surfaces, ANI-1x for organic molecules, or Transition1x for simple organic reactions have emerged, and can be used to train a baseline model, which can subsequently be fine-tuned to a system of interest. Since ab-initio calculations are costly it is nevertheless essential to develop clever data generation strategies, i.e. to identify where a model fails so that new ab-initio calculations can be issued and added to the training data. The quantification of the estimated error in a prediction is furthermore essential for decisionmaking processes. For atomistic simulations, the force uncertainty is key to determine whether the simulation is exploring structures that the machine learning potential is confident about. The identification of high-error predictions is still an open challenge in machine learning across many fields of research, and viable approaches depend on the details of the dataset, task, and model architecture. The error in a model prediction can be dissected into aleatoric (irreducible by addition of data) and epistemic (reducible by addition of data) contributions. The aleatoric cona) Electronic mail: esther.heid@tuwien.ac.at tribution stems from noise in the input data or missing input features, and can be learned by the model itself using heteroscedastic loss models (mean variance estimation) and variations thereof, or can be estimated posthoc. The epistemic contribution is associated with a limited knowledge of the model which can be further dissected into error from model variance and model bias. It is usually approximated by training a committee of models, varying the model initialization seed, hyperparameters, architecture, or training data, and monitoring their disagreement on a prediction to obtain a proxy for the error from model variance. Other techniques furthermore obtain a combined measure for aleatoric and epistemic error. Yet, especially epistemic error is notoriously difficult to model, since the above approaches only capture error from variance, but not from model bias. Yet, model bias can be the major source of error in a model especially for small datasets and difficult-to-learn targets, so that the epistemic uncertainty obtained from committees often underestimates the model error. In the field of machine learning potentials, uncertainty estimation techniques have achieved mixed results. Here, aleatoric uncertainty is usually negligible, since there is a direct, learnable relation between the input (the atomic numbers and coordinates) and the target (the ab-initio energies and forces) if different spin states and magnetic states are not taken into account. However, there is no direct correlation between the epistemic uncertainty of a single data point obtained from the standard deviation of committee predictions and the absolute error. This behavior has been reported for a wide variety of datasets and model architectures, but its origin and possible remedies have not been identified yet. Heuristic approaches to average force uncertainties over structures as a proxy for model error have achieved success in active learning settings, but are missing a theoretical framework and explanation of why and when such an approach is recommendable. Moreover, as simulations based on machine learning potentials are moving towards trillions of atoms and ab-initio calculations become infeasible for the full system, even a perfect estimator of the overall error is insufficient. For active learning it is therefore essential to trace down the overall error and uncertainty to smaller regions within a system, which can then be isolated. While recent approaches to active learning have attributed the uncertainty on a per atom basis, there is no guarantee that the obtained atomic uncertainties actually correlate with the model error. In fact, recent studies reported that there is no direct correlation between the uncertainty of atomic forces and the actual error in the force prediction of that atom. In summary, the unresolved challenges of accurate global and local uncertainty estimates largely hinder the development of efficient active learning cycles for machine learning potentials. Yet, the field has a major advantage over other prediction tasks: Instead of learning to predict a single quantity (one target value per data point), a machine learning potential always predicts a molecular/fullstructure energy, as well as atomic forces for each atom in each spatial direction. In the current study, we discuss why approaches based on aggregating uncertainties over all atoms in a data point give reliable uncertainty estimates, providing a theoretical framework for previous work. We then take the concept a step further and present a new, model-agnostic, simple, and fast method to obtain spatially resolved uncertainties that correlate with the actual error for all atoms within a data point. We detail the theoretical basis of our approach and demonstrate it on diverse systems, namely organic reactions in gas phase, perovskite structures, and liquid water, where we find that global and spatially resolved local model errors can be predicted quantitatively. We then show how our approach enables a reliable identification of spatially resolved high-error regions using simple committee standard deviations and demonstrate the capabilities of these local uncertainties for active learning.