Early detection and precise segmentation of polyps in colonoscopy images are crucial for the timely diagnosis and treatment of colorectal cancer. In this work, we introduce QPolypNet, an innovative hybrid quantum-classical deep learning architecture tailored for medical image segmentation. To the best of our knowledge, this is the first approach to apply quantum-enhanced techniques to polyp segmentation. QPolypNet integrates parameterized quantum circuits as adaptive feature enhancers with a ResNet-50 backbone, a feature pyramid network, and attention mechanisms to boost segmentation performance. Evaluated on four public datasets: CVC-ClinicDB, Kvasir-SEG, CVC-Colon-DB, and ETIS-Larib, our method achieves consistently strong performance across all datasets. Specifically, it yields Dice scores of 0.942 (CVC-ClinicDB), 0.892 (Kvasir-SEG), 0.911 (CVC-Colon-DB), and 0.932 (ETIS-Larib), surpassing state-of-the-art polyp segmentation models. These results highlight the potential of hybrid quantum-classical architectures to enhance medical image analysis and pave the way for further research into quantum-assisted healthcare solutions.