Semi-Supervised Medical Image Segmentation (SSMIS) has significantly reduced the need for manual labeling by utilizing unlabeled data and made considerable progress, there are still issues with errors from noisy pseudo-labels and limited utilization of pseudo-label information. To address these two challenges, we propose a novel Uncertainty-Inspired Credible Pseudo-Labeling (UCPL) framework for SSMIS. UCPL leverages uncertainty estimation, which indicates the reliability of predictions, to guide the Semi-Supervised Learning (SSL) process. Boosted by this uncertainty estimation, UCPL gains from acquiring more reliable pseudo-labels and enhances learning efficiency from unlabeled data. Specifically, our approach starts by estimating uncertainty to obtain uncertainty maps. These uncertainty maps then guide the proposed Class-aware Uncertainty Region-Paste (CURP) and Uncertainty-aware Thresholding (UAT). CURP selectively replaces the most uncertain regions in unlabeled images with matching class regions from labeled images, improving the credibility of pseudo-labels. By considering the model’s real-time learning state through uncertainty, the suggested UAT dynamically adjusts the confidence threshold and balances the involvement of pseudo-labels and the noise contained. Experiments on two public medical image segmentation datasets reveal that our method outperforms existing SSL methods. The code will be released at https://github.com/Duckyee728/UCPL.git .