Although UQ methodologies have been widely applied in ML-assisted prediction for small molecule materials, their systematic evaluation for polymers remains underexplored. Existing studies, such as the application of GPR for UQ on 𝑇 𝑔 of polymers, have demonstrated the potential of UQ to capture data uncertainties. However, such efforts have been narrowing in scope, typically focusing on individual properties or methods. This limited focus leaves a significant gap in understanding the broader applicability and reliability of UQ methodologies in polymer informatics. Polymers, with their inherent structural complexity and diversity, pose unique challenges for ML models, including the representation of molecular structures and the design of architectures capable of addressing this complexity. These challenges are further compounded by the lack of systematic work for evaluating UQ methods across diverse polymer properties and ML approaches. Addressing this gap is essential for advancing ML-driven polymer discovery and design. A tailored and comprehensive approach to UQ is critical to accurately capturing the nuanced behaviors of polymeric materials. Such efforts would enable more reliable and interpretable predictions, guiding experimental validation and reducing resource-intensive efforts.