Colorectal cancer often arises from precancerous polyps, where accurate size assessment is vital for clinical decisions but challenged by subjective methods. While artificial intelligence (AI) has shown promise in improving the accuracy of polyp size estimation, its development depends on large, meticulously annotated datasets. We present Polyp-Size, a dataset of 42 high-resolution white-light colonoscopy videos with polyp sizes precisely measured post-resection using vernier calipers to submillimeter precision. Unlike existing datasets primarily focused on polyp detection or segmentation, Polyp-Size offers validated size annotations, diverse polyp features (Paris classification, anatomical location and histological type), and standardized video formats, enabling robust AI models for size estimation. By making this resource publicly available, we aim to foster research collaboration and innovation in automated polyp measurement to ultimately improve clinical practice.