Deep learning models have gained significant attention due to their promising performance in medical image tasks. However, a gap remains between experimental accuracy and real-world applications. The inherited black-box nature of the deep learning model introduces uncertainty, trustworthy issues, and difficulties in performing quality control of deployed deep learning models. While quality control methods focusing on uncertainty estimation for segmentation tasks exist, there are comparatively fewer approaches for classification, particularly in multi-label datasets. This paper addresses this gap by proposing a quality control method that bridges interpretability and uncertainty estimation through a graph-based class distinctiveness calculation. Using the CheXpert dataset, the proposed approach achieved a higher \(F_1\) score on the bootstrapped test set compared to baselines quality control approaches based on predictive entropy and test-time augmentation.