Segmentation is a key component of several bio-medical image processing systems. Recently, segmentation methods based on supervised learning such as deep convolutional networks have enjoyed immense success for natural image datasets and biological datasets alike. These methods require large volumes of data to avoid overfitting which limits their applicability. In this work, we present a transfer learning mechanism based on active learning which allows us to utilize pre-trained deep networks for segmenting new domains with limited labelled data. We introduce a novel optimization criterion to allow feedback on the most uncertain, yet abundant image patterns thus provisioning for an expert in the loop albeit with minimum amount of guidance. Our experiments demonstrate the effectiveness of the proposed method in improving segmentation performance with very limited labelled data.