With the spatially resolved uncertainties established as proxies for the local prediction error, we in the following explore their use in an active learning scenario. The concept of local uncertainties is most helpful for large systems, where they would enable an active learning loop involving cutting out a fragment of the system for ab-initio calculations, instead of recomputing the full system. Molecular dynamics (MD) simulations pose an important application, where a large simulation cell prohibits the addition of new training data of the full system. Here, we can make use of the direct correlation of local uncertainty with local error to design an active learning framework based on local subregions where a model prediction is identified to fail. Similar approaches have been proposed before, but suffer from selection based on atomic uncertainties, which we show to not correlate with the actual error. Further, the scheme proposed in Ref. 39 only makes use of the forces on the central atom, thus not making use of a large amount of reference data. As detailed in the Methods section, we start with only 50 high energy water structures largely irrelevant to the density and temperature of interest. The active learning cycle is schematically depicted in Fig. . The procedure consists of iteratively selecting sub-regions of the large simulation cell exhibiting large local uncertainty and constructing new small cells for which ab-initio calculations are performed. Thereby, new training data is only added for the relevant subregions. Fig. depicts the performance of the uncertainty-based models on test sets also constructed from MD simulation (see Methods for details). For all test sets the error falls off rapidly as data from the local substructures are added to the training set, with each active-learning cycle adding only ten data points. As comparison, we furthermore randomly selected frames and regions to cut out boxes, as well as based on the atomic, non-aggregated uncertainty. Averaged over all test data points, the accuracy of predicting forces does not differ significantly between the three approaches, because with the little amount of training data available, the addition of any data is helpful to the model. However, the uncertainty-sampling method largely outperforms random and atom-uncertainty sampling for high-energy structures even after only a few active learning cycles. This is especially important for molecular dynamics simulations, where even infrequent wrong predictions of the forces can cause the trajectory to deviate from an ab-initio trajectory significantly, or can cause the simulation to crash. We therefore largely favor a model not only able to predict typical configurations well, but also extrapolate outside of that realm. The identification of ill-predicted environments via the local uncertainty therefore enables an efficient, fast, and effective way to collect new training data in an active learning scheme for molecular dynamics simulation and yields stable models better able to extrapolate.