Despite the model's predictive capability, the average miscalibration plot highlighted an underconfident total uncertainty, with a mean absolute calibration error (MACE) of 0.25. This underconfidence inflated uncertainty estimates across the test dataset (Figure ). To improve this, we applied posthoc calibration techniques using both parametric and non-parametric methods, guided by the validation dataset, to refine uncertainty estimates. Among these, isotonic regression effectively reduced the overall miscalibration area to nearly zero, ensuring well-calibrated predictions on average (Figure ). For comparison, results of standard scaling and GP-Normal calibration methods are provided in the Supplementary Information (Figure ). To further evaluate calibration robustness across different data subsets, we computed group adversarial group calibration (AGC), which assesses performance at varying group sizes. This analysis revealed that while the overall miscalibration was minimized, small group sizes (0.11) still exhibited a nonzero miscalibration area of 0.02, indicating that some residual calibration erro rs persist (Figure ). Additionally, we explored the impact of recalibration on computational efficiency by attempting to sample low-uncertainty predictions (e.g., 2σ i < 1 a 0 2 ; within 95% confidence interval) from uncalibrated outputs. Before recalibration, the predicted uncertainties were systematically overestimated due to underconfidence, preventing the identification of any low -uncertainty points within this range. However, after applying calibration, we successfully identified 3,307 lowuncertainty points, of which only 86 exhibited absolute errors exceeding 1 1 a 0 2 , a remarkably low fraction. This demonstrates that recalibration not only improves uncertainty estimates but also reduces redundant computations by filtering out overestimated uncertainties. Consequently, this enhances the efficiency and cost-effectiveness of downstream analyses, particularly in active learning workflows where computational resources can be prioritized for more uncertain cases.