Initially, the computed uncertainties were not well-calibrated (underconfident), which could lead to redundant calculations in real active learning scenarios. Since neither single -model nor ensemblebased uncertainty estimates inherently guarantee proper calibration, this work emphasizes the importance of posthoc recalibration techniques. To address this, we applied non-parametric (isotonic regression and standard scaling) and parametric (GPNormal) methods to align the predictive distributions with the observed error distribution on unseen data. Given the different uncertainty quantification strategies single-model predictions for scalar properties and ensemble-based models for machine learning potentials calibration played a crucial role in ensuring reliable uncertainty estimates in both cases. Additionally, we evaluated the effectiveness of these calibration techniques using adversarial group calibration, which provides a more detailed assessment compared to average calibration by highlighting robustness across smaller, challenging subsets of the data. Our results demonstrate that posthoc calibration not only improved the reliability of uncertainty estimates but also enhanced the active learning process by reducing redundant queries and effectively identifying underrepresented regions requiring further sampling.