Moreover, the Spearman correlation coefficients between the estimated uncertainty and prediction MAEs for the AdsMT models with different graph encoders consistently exceed 0.98 on the three GMAE datasets (Supplementary Fig. ). The results show that the AdsMT's estimated uncertainty is significantly correlated with the predicted MAE, and its predictions are highly accurate at low uncertainty levels . Furthermore, we investigated whether the AdsMT's uncertainty estimation is well-calibrated and statistically significant, thereby avoiding overconfidence or underconfidence . Sup-plementary Fig. presents the calibration curves and corresponding miscalibration areas of AdsMT models with different graph encoders on the three GMAE datasets (Supplementary Note 5-6), which is an effective approach to evaluating the calibration of uncertainty estimates . It is notable that the calibration curves of AdsMT models closely approximate the ideal diagonal line and exhibit small miscalibration areas less than 0.1. The results prove that AdsMT's uncertainty estimations are well-calibrated and scaled with errors .