Precise delineation of medical images plays a crucial role in advancing automated diagnostic systems and therapeutic strategy development. Despite the advancements in traditional CNN-based segmentation methods, they encounter significant hurdles, primarily the limited capability in capturing long-range dependencies due to the inherent localization of convolution operations, and the reduced segmentation accuracy resulting from uniform down sampling when extracting diverse scale features. We present MPF-Net, an integrated architecture that systematically addresses these limitations through designed to boost the efficiency and robustness of medical image segmentation. MPF-Net is composed of three integral components: (1) a prior information branch that employs super pixels to filter out redundant information and integrate key edge details as prior knowledge; (2) parallel convolution blocks that effectively extract diverse scale features and local context from medical images, accommodating their varying shapes and sizes; and (3) a channel-wise cross-fusion attention block, which is based on Transformer architecture, designed to capture long-range dependencies and diminish semantic gaps. Extensive experiments on three medical image segmentation datasets demonstrate MPF-Net's effectiveness, with DSC scores of 81.84 %, 91.10 %, and 90.73 % achieved on MoNuSeg, GLaS, and ISIC2018 datasets. Further evaluation on the external PH2 dataset yields a DSC of 78.73 %. MPF-Net delivers high-precision and robust generalization capabilities for segmenting complex medical images.