In this article, we study the properties of quantitative steganography detectors (estimators of the payload size) for content-adaptive steganography. In contrast to non-adaptive embedding, the estimator's bias as well as variance strongly depend on the true payload size. Initially, and depending on the image content, the estimator may not react to embedding. With increased payload size, it starts responding as the embedding changes begin to ``spill'' into regions where their detection is more reliable. We quantify this behavior with the concepts of reactive and estimable payloads. To better understand how the payload estimate and its bias depend on image content, we study a maximum likelihood estimator derived for the MiPOD model of the cover image. This model correctly predicts trends observed in outputs of a state-of-the-art deep learning payload regressor. Moreover, we use the model to demonstrate that the cover bias can be caused by a small number of ``outlier'' pixels in the cover image. This is also confirmed for the deep learning regressor on a dataset of artificial images via attribution maps.