Active learning is a strategic approach in machine learning designed to improve the efficiency of data annotation by selecting only the most informative examples for labeling, thus reducing the overall workload and cost. It involves an iterative process where a model is initially trained on a small subset of data, then used to identify which additional data points would be most beneficial to label for further training, continuing until a stopping criterion is met. This approach is particularly valuable in domains where data annotation is costly and time-consuming, such as medical imaging or autonomous driving, where datasets are vast and often redundant. Despite its advantages, active learning presents challenges, including potential biases in data selection, the need for substantial computational resources, and the complexity of integrating an effective pipeline. Alternatives like random subsampling and clustering-based sampling are viable options but may not offer the same targeted benefits. Ultimately, the decision to implement active learning should weigh the trade-offs between reduced annotation costs and the increased complexity and computation required for its execution.