To enhance the accuracy and credibility of existing polyp segmentation methods, this study proposes a reliable segmentation technique that uses local channel attention. First, the improved pyramid vision transformer is employed to extract polyp region features, thereby addressing the insufficient feature extraction capabilities of traditional convolutional neural networks. In addition, a local channel attention mechanism is applied to fuse cascade features, and the edge detail information is gradually recovered to enhance the overall representational capability of the model while ensuring accurate polyp localization. Finally, a trusted polyp segmentation model is developed based on subjective logic evidence to derive the probability and uncertainty of the polyp segmentation problem, and a plausibility measure is applied to the segmentation results. Extensive experiments demonstrate that the proposed approach outperforms state-of-the-art polyp segmentation techniques in terms of accuracy, robustness, and generalization, leading to more reliable polyp segmentation results.