Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Early detection and removal of precancerous polyps can significantly reduce both the incidence and mortality of CRC. Colonoscopy is considered the gold standard for CRC screening; however, its accuracy heavily depends on operator expertise and is susceptible to human errors, fatigue, and inter-observer variability, which can lead to missed or mischaracterized polyps. In this work, we propose an Attention-Guided U-Net model for accurate polyp segmentation in colonoscopy images. Attention gates are incorporated into the skip connections of U-Net to help the network focus on target regions while suppressing background noise. For fair comparison, a vanilla U-Net serves as the baseline, and DeepLabV3++ is used as a state-of-the-art reference. Experiments on publicly available colonoscopy datasets demonstrate that the proposed Attention-Guided U-Net outperforms the baseline models, achieving a validation Dice score of 93.30% and an Intersection over Union (IoU) of 87.53%. High precision and recall further confirm the robustness of the model. Qualitative results show improved boundary delineation, particularly for small, flat, and low-contrast polyps. These results indicate that attention-based feature selection effectively enhances segmentation performance, suggesting the proposed framework as a reliable computer-aided diagnosis (CAD) tool for CRC screening.