Active learning (AL) is a subfield of machine learning exploring different strategies of finding the most informative data points . The aim is to determine which data points maximise the learning and accuracy of a machine learning model. During training, the learning model queries points to be labelled on its own to achieve this goal . One approach to active learning, called pool-based sampling, assumes a small pool of labelled data L and a large pool of unlabelled data U . Labels of unlabelled data are obtained by querying the labels of data points of U from an oracle, e.g., previously known results or conducted experiments. These data points are then added to the pool of labelled data L and used to improve the model, as illustrated in Figure . The goal in this case is to find the optimal model with the smallest possible pool of labelled data since each query of labels is associated with a cost, such as the cost of conducting an experiment. The problem is then to determine the data points that are most informative and whose labels therefore should be queried from the oracle. The data points that should be included in each query are determined by an acquisition function that estimates the informativeness of each data point. One popular type of active learning strategy is uncertainty sampling where the most uncertain data points, i.e., the data points the learning model knows the least about, are seen as most informative. A pre-determined acquisition function then determines the most uncertain batch of unlabelled data points whose labels should be queried from the oracle. Thus, the data points that the model is already confident about are avoided.