Protein expression analysis has traditionally relied upon visual evaluation of immunohistochemical reaction by a pathologist, who analyzes the grade of staining intensity and estimates the percentage of cells stained in the area of interest. This method is effective in experienced hands but has potential limitations in its reproducibility due to subjectivity between and within operators. These limitations are particularly pronounced in gray areas where a distinction of weak from moderate protein expression can be clinically significant. Some research also suggests that sub localization of the protein expression into different components such as nuclei versus cytoplasm may be of great importance. This distinction can be particularly difficult to quantify using manual methods. In this paper, we formulate the problem of quantitative protein expression analysis as an active learning classification problem, where a very small set of pre-sampled user data is used for understanding expert evaluation. The expert coveted confidence is mapped to derive an uncertainty region to select the supplemental learning data. This is done by posing a structured query to the unknown data set. The newly identified samples are then augmented to the training set for incremental learning. The strength of our algorithm is measured in its ability to learn with minimum user interaction. Chroma analysis results of a Tissue Micro-array (TMA) images are presented to demonstrate the user interaction and learning ability. The chroma analysis results are then processed to obtain quantitative results.