Visualizing Variance / Standard Deviation for categories
I know this may not exactly correspond to what you need, but my first idea would not be to visualize the variances. Instead, I would define a metric for each method.

For instance, considering all results obtained with a given, you have a vector of outputs (one Variance for X for each Category), then you can simply compute a p-norm of this vector, and compare it to the norm obtained for other methods:

$$\left\lVert x\right\rVert_p = \left( \sum_{i=1}^n x_i^p \right)^{1/p} $$

$p=2$ gives you the euclidian norm, you can increase or decrease $p$ depending on if you want to raise attention on maximal values or the average.

In terms of visualization, you could simply plot (in a bar plot for instance) a few norms that you selected (orders 1, 2, and infinite for instance).



For a pure visualization, you could plot the data in violin plots. For instance in python:

import seaborn as sns
sns.violinplot(x="Method", y="Variance for X", data=your_data_as_df)


Each violin would give you an idea of how the data is distributed over all categories.