For the WS22 dataset, deep ensembles demonstrated their utility in handling complex configurational spaces and distribution shifts. The calibration of uncertainty estimates addressed significant underconfidence in both energy and force predictions, particu larly for underrepresented regions such as cis conformers and geodesic interpolation points. Posthoc calibration significantly improved computational efficiency by increasing the number of low-uncertainty predictions while avoiding redundant evaluations. For energy predictions, the ISR method identified ~13% more lowuncertainty points compared to the uncalibrated model, reducing the need for 720 redundant evaluations. For force predictions, ISR enabled over 4500 additional low -uncertainty predictions, drastically reducing the computational cost associated with high -confidence sampling in molecular systems. Calibrated uncertainty predictions demonstrated strong correlations with prediction errors, reliably identifying challenging regions in the configurational space. These regions, often associated with high uncertainty, can be prioritized for future sampling in active learning workflows, thereby enhancing model generalization and reducing training bias.