Argument Based Machine Learning (ABML) was recently demonstrated to offer significant benefits for knowledge elicitation. In knowledge acquisition, ABML is used by a domain expert in the so-called ABML knowledge refinement loop. This draws the expert's attention to the most critical parts of the current knowledge base, and helps the expert to argue about critical concrete cases in terms of the expert's own understanding of such cases. Knowledge elicited through ABML refinement loop is therefore more consistent with expert's knowledge and thus leads to more comprehensible models in comparison with other ways of knowledge acquisition with machine learning from examples. Whereas the ABML learning method has been described elsewhere, in this paper we concentrate on detailed mechanisms of the ABML knowledge refinement loop. We illustrate these mechanisms with examples from a case study in the acquisition of neurological knowledge, and provide quantitative results that demonstrate how the model evolving through the ABML loop becomes increasingly more consistent with the expert's knowledge during the process.