In this work, we systematically evaluate six ML models for quantifying uncertainties in predicting diverse polymer properties, as shown in Table , including 𝑇 𝑔 , 𝐸 𝑔 , melting temperature (𝑇 𝑚 ) and decomposition temperature (𝑇 𝑑 ) of linear polymer. Besides, this study includes various datasets for 𝑇 𝑔 , including out-of-distribution (OOD) polymers, high-𝑇 𝑔 polymers and specific polymer types from 𝑇 𝑔 dataset, providing a comprehensive assessment of UQ methods across different scenarios. The molecular structures of polymers are represented using simplified molecular input line entry system (SMILES) notations, which describe the connectivity and substructures of monomer repeat units. Morgan fingerprint with frequency (MFF) is used as the input format, encoding the occurrence patterns of specific substructures in the polymers. Three independent metrics, including accuracy (R 2 ), Spearman's rank correlation coefficient and calibration, are employed to evaluate the performance of the ML models, forming a robust benchmark for UQ in polymer property prediction. The workflow of this study is illustrated in Figure .