In medical imaging, the acquired images are usually analyzed by a human observer and rated with respect to a diagnostic question. However, this procedure is time-demanding and expensive. Furthermore, the lack of a reference image makes this task challenging. In order to support the human observer in assessing image quality and to ensure an objective evaluation, we extend in this paper our previous no-reference magnetic resonance (MR) image quality assessment system with an active learning loop to reduce the amount of necessary labeled training data. We employ two different active learning query strategies based on uncertainty sampling. Since the classification task is performed on 2D image slices, but the human observer labels complete 3D image volumes, we present a method to select representative 3D images instead of independant 2D image slices. The performance is evaluated on in-vivo MR image data.