Outlier detection (OD) aims to identify abnormal instances, known as outliers or anomalies, by learning typical patterns of normal data, or inliers. Performing OD under an unsupervised regime--without any information about anomalous instances in the training data--is challenging. A recently observed phenomenon, known as the $\textit{inlier-memorization (IM) effect}$, where deep generative models (DGMs) tend to memorize inlier patterns during early training, provides a promising signal for distinguishing outliers. However, existing unsupervised approaches that rely solely on the IM effect still struggle when inliers and outliers are not well-separated or when outliers form dense clusters. To address these limitations, we incorporate $\textit{active learning}$ to selectively acquire informative labels, and propose $\textit{IMBoost}$, a novel framework that explicitly reinforces the IM effect to improve outlier detection. Our method consists of two stages: 1) a $\textit{warm-up}$ phase that induces and promotes the IM effect, and 2) a $\textit{polarization}$ phase in which actively queried samples are used to maximize the discrepancy between inlier and outlier scores. In particular, we propose a novel query strategy and tailored loss function in the polarization phase to effectively identify informative samples and fully leverage the limited labeling budget. We provide a theoretical analysis showing that the IMBoost consistently decreases inlier risk while increasing outlier risk throughout training, thereby amplifying their separation. Extensive experiments on diverse benchmark datasets demonstrate that IMBoost not only significantly outperforms state-of-the-art active OD methods but also requires substantially less computational cost.