Active learning is a strategy designed to reduce annotation costs by allowing models to select the most informative samples from an unlabeled dataset, particularly in tasks like image classification. Integrated within the broader Human-in-the-loop framework, active learning creates an interactive process where human annotators play a role in labeling data. However, this interaction introduces variability in annotation quality, since annotators may differ in domain knowledge, experience, and reliability. Traditional active learning approaches often overlook these differences, assuming constant performance across all annotators. This assumption can lead to suboptimal model updates, especially when labels come from less reliable sources. To tackle this limitation, this work proposes a reputation-based framework that captures annotator performance over time and across domains. Reputation scores are computed based on past annotation accuracy and feedback from other annotators, and these scores are then used to weigh individual contributions through a voting mechanism. In addition, the study explores how annotator expertise and oracle size influence the effectiveness of this approach, evaluating their impact on model performance in controlled, simulated settings with imperfect annotators.