This paper introduces SCC-CAM, a novel weakly supervised segmentation (WSSS) method for medical images. Transformer-based methods frequently face challenges like over-activation and inaccuracy in generating class attention maps (CAM), especially noticeable in medical images. We found that the attention mechanism tends to create excessive similarity among patch tokens, leading to over-activation in class attention maps. Moreover, potential confounders in medical images severely impair the localization of classes. To tackle these challenges, we propose a method named SCC-CAM based on similarity constraint and causality for generating class attention maps. This method directly constrains the similarity between patch tokens to guide the precise localization of objects. Additionally, we remove the influence of potential confounders by introducing causal theory, further enhancing the accuracy of results. Compared to other WSSS methods, our SCC-CAM achieves the best pseudo masks with Dice scores of 68.2%, 72.8% and 77.1% on the T1, T2 and T2-FLAIR modalities of the BraTS 2021 dataset. The evaluation on the BraTS 2021 dataset demonstrates the effectiveness and superiority of our proposed method.