The total uncertainty for a prediction results from the summation of the aleatoric uncertainty (arising from noise in the data) and epistemic uncertainty (stemming from model limitations). It is reasonable to expect predictions with lower total uncertainty to be more likely true. To investigate whether a negative correlation exists between total uncertainty and the model's predictive performance, we divided the test set atoms into 20 bins based on their total uncertainty scores. The number of bins was chosen to balance granularity with statistical significance. For each bin, we computed the average total uncertainty, the accuracy, and the Spearman rank-order correlation coefficient ρ to assess the relationship between these values. Figure shows the average total uncertainty versus accuracy. The strong negative correlation (ρ = -0.96) between total uncertainty and accuracy indicates that the model's confidence aligns well with its predictive performance. A similar study based on the MCC is available in the Supporting Information, section S.8.