###### Abstract
To reduce the dependence of SAM fine-tuning on precise pixel-level annotation data, this study proposes a SAM method for colonoscopy polyp segmentation called WSPolyp-SAM, based on weak supervision and self-guided fine-tuning. Specifically, the SAM method is guided to generate segmentation masks based on weak annotations (bounding box annotations in this study), which are then treated as pseudo-labels to guide the fine-tuning of SAM. Utilizing self-generated pseudo-labels for fine-tuning not only decreases the dependence on precisely annotated data but also avoids the need to introduce additional segmentation models. High-quality pseudo-labels provide additional information, helping the model to better capture specific features. To enhance the reliability and accuracy of pseudo-labels, this study designs three enhancement strategies. Firstly, employing a multi-augmentation fusion strategy involves generating multiple augmented views for each image and then fusing their corresponding segmentation masks. This approach highlights reliable prediction results and counteracts biases introduced by image augmentation, thereby enhancing the quality of the final fused mask. Secondly, incorporating a pixel-level weighting strategy involves assigning higher weights to pixels with high certainty predictions through an entropy-based pixel-level weighting mechanism, further improving the accuracy of the segmentation mask. Finally, we introduce mask post-processing techniques that can diminish potential noise and inaccuracies in the masks generated by SAM. These three strategies aim to optimize the details of the segmentation masks and improve their consistency with ground-truth labels. The main contributions of this study include:  
* The proposal of a novel weakly supervised and self-guided fine-tuning method of SAM for colonoscopy polyp segmentation (abbreviated as WSPolyp-SAM). This method reduces the dependence on precise annotations by fully utilizing SAM's zero-shot ability to use weak annotations for guiding the generation of segmentation masks, which are then utilized as pseudo-labels for self-guided fine-tuning, avoiding the need to introduce additional segmentation models. * The introduction of a series of pseudo-label enhancement strategies to generate high-quality pseudo-labels. These enhancement strategies, including multi-enhancement fusion, pixel-level weighting, and mask post-processing techniques, enable the acquisition of more accurate pseudo-labels. * Experimental results on five medical image datasets demonstrate that WSPolyp-SAM outperforms current fully supervised mainstream polyp segmentation networks on the Kvasir-SEG, ColonDB, CVC-300, and ETIS datasets. Particularly, on the ColonDB dataset, our method demonstrated an improvement of 9.4% in mDice score and 9.6% in mIoU score compared to state-of-the-art networks, representing a significant breakthrough in the field of colonoscopy polyp segmentation. Furthermore, by using different amounts of training data in weakly supervised and fully supervised experiments, it is found that weakly supervised fine-tuning can save 70% to 73% of the cost of annotation time compared to fully supervised fine-tuning. The remaining sections of this paper are structured as follows: Section 2 discusses related work, Section 3 elaborates on the methods employed in our study, Section 4 details the dataset and conducts the experimental discoveries, and finally, Section 5 delivers the concluding remarks of our study.