Medical image segmentation is crucial for clinical decision making and treatment planning. However, it faces two challenges: First, the structures of salient objects and background details vary significantly in medical images of different modalities. Second, the misleading co-occurrence of salient and non-salient objects and the noise interference at the edges affect the segmentation accuracy of the model. To overcome these challenges, we propose RoSPER-Net, a framework designed to enhance medical image segmentation. RoSPER-Net integrates a Spatial Prompt Encoder (SPE), which generates two complementary prompts using an advanced prompt mechanism to guide the model to focus on the local-global structure of salient objects and understand the overall background information in the image, thereby improving the model’s adaptability and segmentation accuracy under different modalities and complex backgrounds. Plus, our Cross-Scale Edge Enhancement Decoder (CSED) uses noise suppression and edge enhancement mechanisms to suppress non-salient regions and highlight salient regions, thereby improving the model’s ability to detect salient objects in complex backgrounds. Comprehensive evaluations of RoSPER-Net on 5 medical image datasets verify its superior performance and versatility, demonstrating its potential in the field of medical image segmentation. Our code is available on https://github.com/Anonymous2025Paper/RoSPER-Net .