The classification of endoscopy images is vital for early detection and prevention of Colorectal Cancer (CRC). However, manual annotation of these images is expensive. Semi-supervised Active Learning (SAL) can help reduce costs, but issues with the accuracy of pseudo-labels and the tendency to over-select outliers remain. To address these, we introduce ROSAL, a new SAL framework featuring Representational Correlation-based Pseudo-label Training (RCPT) and Outlier-based Hybrid Querying (OHQ). RCPT employs a pseudo-label contrastive loss to enhance agreement among unlabeled data representations and reduce discord. The pseudo-label generator in RCPT leverages this correlation for more precise labeling. OHQ introduces a distance factor to minimize outlier selection through a hybrid querying strategy. Experimental results demonstrate that ROSAL outperforms other active learning methods, achieving 71.46 \(\%\) and 90.79 \(\%\) accuracy on a publicly available endoscopic dataset and a publicly available natural image dataset, respectively, using only 40 \(\%\) and 20 \(\%\) of the labeled data.