Monitoring human activities' impact on soil biodiversity over time is a costly and resource-intensive challenge. Modern technologies like deep learning offer a promising solution due to their capability to analyze large datasets much faster than humans. However, deep learning relies on extensively annotated datasets, and annotating these samples is both time-consuming and expensive, complicating its application. This paper introduces a novel active learning approach called Active Learning Extension (ALE), which aims at improving model performance in object detection tasks while minimizing the need for extensive data annotation. Traditional active learning methods typically rely solely on prediction uncertainty to select images for annotation, which can be suboptimal when introducing new classes. ALE addresses this limitation by considering both the uncertainty and the number of predictions. This dual consideration leads to significant improvements, particularly in scenarios like Collembola detection, where creating and updating datasets is highly timeintensive. Our evaluation demonstrates that ALE significantly enhances model performance compared to stateof-the-art methods. The results underscore the importance of selecting challenging examples and accounting for the number of predictions to optimize active learning in object detection.