Medical image segmentation has become increasingly critical and urgent in supporting diagnostic processes. Over the years, with the rapid advancement of technology, the application of deep learning in diagnostic assistance has gained substantial traction. Notably, Mamba has recently garnered significant attention in the medical image segmentation domain following its notable success in natural language processing. Leveraging Mamba’s exceptional capabilities, this study introduces a novel medical image segmentation model, Lite-SCMamba, which is built upon the foundation of Mamba and Convolutional Neural Networks (CNN). Additionally, the Priority Attention Mamba (PA-Mamba) block is proposed to enhance the feature extraction capabilities of both the encoder and decoder. To evaluate the performance, we conducted experiments on two benchmark datasets, ISIC18 and DSB, achieving promising results. The code for our method will be made publicly available at: https://github.com/kwanghwi242/SegMamModel .