Accurate polyp segmentation during colonoscopy is crucial for the early detection and timely intervention of colorectal cancer. Recently, Mamba, a State Space Model, has gained significant attention in polyp segmentation due to its remarkable ability to model long-range dependencies with linear computational complexity. However, Mamba-based methods face two key challenges: (1) their fixed scanning pattern limits the capture of dynamic spatial context, impairing the precise localization of irregular polyps; (2) during the calculation process, the high-frequency information that is crucial to local details is weakened, and the blurred mid-frequency information becomes dominant, thereby reducing the boundary accuracy. To overcome these limitations, we propose PolyMamba, a novel framework that integrates spatial priors while enhancing high-frequency information for more accurate polyp segmentation. Specifically, our framework introduces a Spatial-Prior Guided module, which leverages explicit spatial priors extracted from Transformer-based methods to counteract the local perception bias caused by Mamba’s fixed scanning pattern. Additionally, we design a Dual-Gate Frequency Enhancement module, which applies two Gaussian filters to generate spectra with different high-frequency thresholds, and uses the difference between them as an attention map to selectively enhance high-frequency features, thereby refining the polyp boundaries. Comprehensive experiments on five widely used polyp segmentation datasets demonstrate that PolyMamba not only surpasses existing state-of-the-art techniques but also provides a novel frequency-domain perspective, offering new insights into improving segmentation performance.