In this study, we use R 2 , Spearman's rank correlation coefficient and calibration area as the primary metrics for evaluating UQ models. These metrics operate on similar scales, ensuring consistency in the evaluation process. This combination of metrics forms a robust framework for assessing both the predictive accuracy and the quality of uncertainty estimates, ensuring that UQ models are evaluated holistically and rigorously. By emphasizing confidence estimation, this study advances the development and application of UQ methods across diverse ML tasks, including but not limited to polymer discovery.