Leveraging molecular simulation within active learning frameworks is critical for expediting the discovery of new polymers. Typically, potential candidates are selected from an unlabeled dataset based on predictions made by ML models trained on labeled data. This selection process in active learning is contingent upon the UQ of the predicted target properties. Thus, employing UQ methods to assess performance on unlabeled data, derived from molecular simulations, constitutes a significant facet of the evaluation process. This approach not only refines the selection criteria for potential polymer candidates but also enhances the efficiency and accuracy of the active learning cycle, thereby streamlining the development of innovative polymer materials.