Histopathology image analysis plays a vital role in disease diagnosis; however, the limited availability of labeled data and the large size of Whole Slide Images (WSIs) creates a challenge for training deep learning models. Traditional active learning methods, which typically select entire images for labeling, are impractical for WSIs due to their substantial size and computational requirements. This paper presents a novel active learning framework specifically tailored for WSI selection. Our method computes WSI-level uncertainty scores using various techniques, including Margin Sampling, Monte Carlo Sampling, Gradient-based methods, and diversity scores derived from Cosine similarity and K-means++. By focusing on the most informative WSIs, we aim to significantly reduce labeling efforts while maximizing information gain for subsequent model training. We demonstrate that our approach consistently outperforms random data selection strategies, resulting in significant improvements in segmentation Dice scores on the PANDA dataset for Gleason-grade segmentation. Our work offers a new approach to efficiently use limited labeled data, facilitating the development of more accurate and cost-effective Gleason-grade segmentation models.