  Most of the sophisticated AI models utilize huge amounts of annotated data
and heavy training to achieve high-end performance. However, there are certain
challenges that hinder the deployment of AI models "in-the-wild" scenarios,
i.e., inefficient use of unlabeled data, lack of incorporation of human
expertise, and lack of interpretation of the results. To mitigate these
challenges, we propose a novel Explainable Active Learning (XAL) model,
XAL-based semantic segmentation model "SegXAL", that can (i) effectively
utilize the unlabeled data, (ii) facilitate the "Human-in-the-loop" paradigm,
and (iii) augment the model decisions in an interpretable way. In particular,
we investigate the application of the SegXAL model for semantic segmentation in
driving scene scenarios. The SegXAL model proposes the image regions that
require labeling assistance from Oracle by dint of explainable AI (XAI) and
uncertainty measures in a weakly-supervised manner. Specifically, we propose a
novel Proximity-aware Explainable-AI (PAE) module and Entropy-based Uncertainty
(EBU) module to get an Explainable Error Mask, which enables the machine
teachers/human experts to provide intuitive reasoning behind the results and to
solicit feedback to the AI system via an active learning strategy. Such a
mechanism bridges the semantic gap between man and machine through
collaborative intelligence, where humans and AI actively enhance each other's
complementary strengths. A novel high-confidence sample selection technique
based on the DICE similarity coefficient is also presented within the SegXAL
framework. Extensive quantitative and qualitative analyses are carried out in
the benchmarking Cityscape dataset. Results show the outperformance of our
proposed SegXAL against other state-of-the-art models.
