A world without digital images is unthinkable in this era of information and communication technology. Billions of images are created, shared and ultimately enjoyed by users every day. However, digital images are sensitive to a wide variety of distortions during the delivery mechanism it goes through. Any of the distortions that can arise during the delivery might disrupt the perceptual quality of an image and can incite dissatisfaction from the user. Thus, it is important to optimize the delivery pipeline towards the arrangement of perceptually good results. For that the perceptual image quality needs to be estimated. Image quality assessment (IQA) is concerned with measuring the degradation in quality of images. The level of degradation needs to be measured in a way that is compliant to how humans would perceive it as humans are the final judges of the delivered quality of an image. General purpose image quality metrics are based on a two-step design. First, quality-aware features are extracted, describing artifact appearance to the Human Visual System. Secondly, these features are mapped to a perceived quality score. General purpose metrics require a large number of varied examples of pairs of distorted images and perceived quality scores to acquire an accurate distortion-agnostic quality prediction. Obtaining a distorted image is relatively simple. However, acquiring the quality assessment is a complex, laborious and expensive undertaking. Consequently, all of the widely known image quality datasets only accommodate at most several thousand examples which is in stark contrast to the mound of possible distortion types and distortion intensities there could be. This limitation hinders the creation of an accurate general-purpose image quality assessment metrics. It would be desirable to extend these datasets with more subjectively evaluated images, but given the cost of doing so, one may want to have a smart mechanism to select those that are most informative to improve the accuracy and robustness of quality metrics. Active learning is such a smart selection mechanism and could therefore be a possible solution. The main goal of this thesis is to explore whether active learning can be beneficial to improve the accuracy of image quality metrics.