In this study, we systematically evaluated six state-of-the-art UQ methods in ML for predicting 𝑇 ! values of homopolymers, specifically ensemble, MCD, GPR, MVE, BNN, and EDL. These methods were rigorously assessed using three datasets and high-𝑇 ! experimental data, employing widely recognized criteria for mean and standard deviation. Initially, the accuracy of the mean predictions was examined to ascertain the models' traditional predictive capabilities. Subsequently, we explored three metrics to elucidate the relationship between actual errors and uncertainties, utilizing various evaluation strategies.